{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "34qVD_ntSKLF"
   },
   "source": [
    "# Learning to optimize parametric Quadratic Programming (pQP) and  Quadratically Constrained QP (pQCQP) problems using Neuromancer.\n",
    "\n",
    "\n",
    "This is an interactive notebook based on the python script [Part_2_LearnToOptimize_pQP.py](./Part_2_LearnToOptimize_pQP.py).  \n",
    "\n",
    "Problem formulation pQP:\n",
    "$$\n",
    "    \\begin{align}\n",
    "    &\\text{minimize } &&   x^2 + y^2\\\\\n",
    "    &\\text{subject to} &&  -x - y + p1 \\le 0\\\\\n",
    "    &  &&    x + y - p1 - 5 \\le 0\\\\\n",
    "    &  &&    x - y + p2 - 5 \\le 0\\\\\n",
    "    &  &&   -x + y - p2  \\le 0\\\\\n",
    "    \\end{align}\n",
    "$$\n",
    "\n",
    "\n",
    "Problem formulation pQCQP:\n",
    "$$\n",
    "    \\begin{align}\n",
    "    &\\text{minimize } &&   x^2 + y^2\\\\\n",
    "    &\\text{subject to} &&  -x - y + p1 \\le 0\\\\\n",
    "    &  &&     x^2 + y^2 \\le p2^2\\\\\n",
    "    \\end{align}\n",
    "$$\n",
    "\n",
    "with parameters $p, a$ and decision variables $x, y$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OCn3zpaIqgMc"
   },
   "source": [
    "## NeuroMANCER and Dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qzy5Wot5k2Gf"
   },
   "source": [
    "### Install (Colab only)\n",
    "Skip this step when running locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in c:\\users\\drgo694\\anaconda3\\lib\\site-packages (3.5.1)\n",
      "Collecting matplotlib\n",
      "  Downloading matplotlib-3.7.1-cp39-cp39-win_amd64.whl (7.6 MB)\n",
      "Collecting contourpy>=1.0.1\n",
      "  Downloading contourpy-1.0.7-cp39-cp39-win_amd64.whl (160 kB)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\drgo694\\anaconda3\\lib\\site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\drgo694\\anaconda3\\lib\\site-packages (from matplotlib) (3.0.4)\n",
      "Requirement already satisfied: numpy>=1.20 in c:\\users\\drgo694\\anaconda3\\lib\\site-packages (from matplotlib) (1.21.5)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\drgo694\\anaconda3\\lib\\site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\drgo694\\anaconda3\\lib\\site-packages (from matplotlib) (21.3)\n",
      "Collecting importlib-resources>=3.2.0\n",
      "  Downloading importlib_resources-5.12.0-py3-none-any.whl (36 kB)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\drgo694\\anaconda3\\lib\\site-packages (from matplotlib) (1.3.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\drgo694\\anaconda3\\lib\\site-packages (from matplotlib) (9.0.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\drgo694\\anaconda3\\lib\\site-packages (from matplotlib) (4.25.0)\n",
      "Requirement already satisfied: zipp>=3.1.0 in c:\\users\\drgo694\\anaconda3\\lib\\site-packages (from importlib-resources>=3.2.0->matplotlib) (3.7.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\drgo694\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "Installing collected packages: importlib-resources, contourpy, matplotlib\n",
      "  Attempting uninstall: matplotlib\n",
      "    Found existing installation: matplotlib 3.5.1\n",
      "    Uninstalling matplotlib-3.5.1:\n",
      "      Successfully uninstalled matplotlib-3.5.1\n",
      "Successfully installed contourpy-1.0.7 importlib-resources-5.12.0 matplotlib-3.7.1\n"
     ]
    }
   ],
   "source": [
    "!pip install neuromancer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LWyvndXlz0Fv"
   },
   "source": [
    "### Import"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "KbP0n-4evRqt",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:31:42.664681Z",
     "start_time": "2025-06-26T18:31:29.766786Z"
    }
   },
   "source": [
    "import cvxpy as cp\n",
    "import numpy as np\n",
    "import time\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import neuromancer.slim as slim\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patheffects as patheffects"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "POL27EJZxJmI",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:31:42.681163Z",
     "start_time": "2025-06-26T18:31:42.668697Z"
    }
   },
   "source": [
    "from neuromancer.trainer import Trainer\n",
    "from neuromancer.problem import Problem\n",
    "from neuromancer.constraint import variable\n",
    "from neuromancer.dataset import DictDataset\n",
    "from neuromancer.loss import PenaltyLoss\n",
    "from neuromancer.modules import blocks\n",
    "from neuromancer.system import Node"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_WH7o7Wu1epw"
   },
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "_r6p2p6myHAh",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:31:42.814502Z",
     "start_time": "2025-06-26T18:31:42.800119Z"
    }
   },
   "source": [
    "data_seed = 408  # random seed used for simulated data\n",
    "np.random.seed(data_seed)\n",
    "torch.manual_seed(data_seed)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x25d8b7149b0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-26T18:31:42.846225Z",
     "start_time": "2025-06-26T18:31:42.832393Z"
    }
   },
   "source": [
    "# select from 'pQP' or 'pQCQP'\n",
    "problem_type = 'pQP'   "
   ],
   "outputs": [],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JZ9qrw0tlJhs"
   },
   "source": [
    "Randomly sample parameters from a uniform distribution: $0.5\\le p\\le2.0$;  $0.2\\le a\\le1.2$"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "Nu58M-8JyHy6",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:31:42.871231Z",
     "start_time": "2025-06-26T18:31:42.861383Z"
    }
   },
   "source": [
    "nsim = 3000  # number of datapoints: increase sample density for more robust results\n",
    "# create dictionaries with sampled datapoints with uniform distribution\n",
    "p_low, p_high = 1.0, 11.0\n",
    "samples_train = {\"p1\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high),\n",
    "                 \"p2\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
    "samples_dev = {\"p1\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high),\n",
    "               \"p2\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
    "samples_test = {\"p1\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high),\n",
    "               \"p2\": torch.FloatTensor(nsim, 1).uniform_(p_low, p_high)}\n",
    "# create named dictionary datasets\n",
    "train_data = DictDataset(samples_train, name='train')\n",
    "dev_data = DictDataset(samples_dev, name='dev')\n",
    "test_data = DictDataset(samples_test, name='test')\n",
    "# create torch dataloaders for the Trainer\n",
    "train_loader = torch.utils.data.DataLoader(train_data, batch_size=32, num_workers=0,\n",
    "                                           collate_fn=train_data.collate_fn, shuffle=True)\n",
    "dev_loader = torch.utils.data.DataLoader(dev_data, batch_size=32, num_workers=0,\n",
    "                                         collate_fn=dev_data.collate_fn, shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(test_data, batch_size=32, num_workers=0,\n",
    "                                         collate_fn=test_data.collate_fn, shuffle=True)\n",
    "# note: training quality will depend on the DataLoader parameters such as batch size and shuffle"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-26T18:31:43.071633Z",
     "start_time": "2025-06-26T18:31:42.889425Z"
    }
   },
   "source": [
    "# visualize taining and test samples for 2D parametric space\n",
    "a_train = samples_train['p1'].numpy()\n",
    "p_train = samples_train['p2'].numpy()\n",
    "a_dev = samples_dev['p1'].numpy()\n",
    "p_dev = samples_dev['p2'].numpy()\n",
    "plt.figure()\n",
    "plt.scatter(a_train, p_train, s=2., c='blue', marker='o')\n",
    "plt.scatter(a_dev, p_dev, s=2., c='red', marker='o')\n",
    "plt.title('Sampled parametric space for training')\n",
    "plt.xlim(p_low, p_high)\n",
    "plt.ylim(p_low, p_high)\n",
    "plt.grid(True)\n",
    "plt.xlabel('p1')\n",
    "plt.ylabel('p2')\n",
    "plt.legend(['train', 'test'], loc='upper right')\n",
    "plt.show()\n",
    "plt.show(block=True)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q6DqbTdL6S4o"
   },
   "source": [
    "# pQP Formulation in NeuroMANCER"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y2htUaWMDjsk"
   },
   "source": [
    "## Primal Solution Map Architecture"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "Ta_I_pjyyLzf",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:31:43.119345Z",
     "start_time": "2025-06-26T18:31:43.103265Z"
    }
   },
   "source": [
    "# define neural architecture for the solution map\n",
    "func = blocks.MLP(insize=2, outsize=2,\n",
    "                bias=True,\n",
    "                linear_map=slim.maps['linear'],\n",
    "                nonlin=nn.ReLU,\n",
    "                hsizes=[80] * 4)\n",
    "# wrap neural net into symbolic representation of the solution map via the Node class: sol_map(xi) -> x\n",
    "sol_map = Node(func, ['p1', 'p2'], ['x'], name='map')\n",
    "# trainable components of the problem solution\n",
    "components = [sol_map]"
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lxj77EFj7EO-"
   },
   "source": [
    "## Objective and Constraints in NeuroMANCER"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "bcoVjphjyPp9",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:31:43.182736Z",
     "start_time": "2025-06-26T18:31:43.151544Z"
    }
   },
   "source": [
    "\"\"\"\n",
    "variable is a basic symbolic abstraction in Neuromancer\n",
    "   x = variable(\"variable_name\")                      (instantiates new variable)  \n",
    "variable construction supports:\n",
    "   algebraic expressions:     x**2 + x**3 + 5     (instantiates new variable)  \n",
    "   slicing:                   x[:, i]             (instantiates new variable)  \n",
    "   pytorch callables:         torch.sin(x)        (instantiates new variable)  \n",
    "   constraints definition:    x <= 1.0            (instantiates Constraint object) \n",
    "   objective definition:      x.minimize()        (instantiates Objective object) \n",
    "to visualize computational graph of the variable use x.show() method          \n",
    "\"\"\"\n",
    "\n",
    "# variables\n",
    "x = variable(\"x\")[:, [0]]\n",
    "y = variable(\"x\")[:, [1]]\n",
    "# sampled parameters\n",
    "p1 = variable('p1')\n",
    "p2 = variable('p2')\n",
    "\n",
    "# objective function\n",
    "f = x ** 2 + y ** 2\n",
    "obj = f.minimize(weight=1.0, name='obj')\n",
    "objectives = [obj]\n",
    "\n",
    "# constraints\n",
    "Q_con = 100.\n",
    "g1 = -x - y + p1\n",
    "con_1 = Q_con * (g1 <= 0)\n",
    "con_1.name = 'c1'\n",
    "if problem_type == 'pQP':  # constraints for QP\n",
    "    g2 = x + y - p1 - 5\n",
    "    con_2 = Q_con*(g2 <= 0)\n",
    "    con_2.name = 'c2'\n",
    "    g3 = x - y + p2 - 5\n",
    "    con_3 = Q_con*(g3 <= 0)\n",
    "    con_3.name = 'c3'\n",
    "    g4 = -x + y - p2\n",
    "    con_4 = Q_con*(g4 <= 0)\n",
    "    con_4.name = 'c4'\n",
    "    constraints = [con_1, con_2, con_3, con_4]\n",
    "elif problem_type == 'pQCQP':  # constraints for QCQP\n",
    "    g2 = x**2+y**2 - p2**2\n",
    "    con_2 = Q_con*(g2 <= 0)\n",
    "    con_2.name = 'c2'\n",
    "    constraints = [con_1, con_2]"
   ],
   "outputs": [],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 496
    },
    "id": "n7VPa9Wc8JRB",
    "outputId": "0da17c45-6370-4f46-f626-bd5686b94bfc",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:31:43.214712Z",
     "start_time": "2025-06-26T18:31:43.200490Z"
    }
   },
   "source": [
    "# create penalty method loss function\n",
    "loss = PenaltyLoss(objectives, constraints)\n",
    "# construct constrained optimization problem\n",
    "problem = Problem(components, loss)"
   ],
   "outputs": [],
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "icWSMeG28SKc"
   },
   "source": [
    "## Parametric Problem Solution in NeuroMANCER"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "rk1bRczByUvl",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:31:43.265109Z",
     "start_time": "2025-06-26T18:31:43.251765Z"
    }
   },
   "source": [
    "lr = 0.001      # step size for gradient descent\n",
    "epochs = 400    # number of training epochs\n",
    "warmup = 100    # number of epochs to wait before enacting early stopping policy\n",
    "patience = 100  # number of epochs with no improvement in eval metric to allow before early stopping"
   ],
   "outputs": [],
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "x4oS2N2ZyWtD",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:31:43.313074Z",
     "start_time": "2025-06-26T18:31:43.297114Z"
    }
   },
   "source": [
    "optimizer = torch.optim.AdamW(problem.parameters(), lr=lr)\n",
    "\n",
    "# define trainer\n",
    "trainer = Trainer(\n",
    "    problem,\n",
    "    train_loader,\n",
    "    dev_loader,\n",
    "    test_loader,\n",
    "    optimizer,\n",
    "    epochs=epochs,\n",
    "    patience=patience,\n",
    "    warmup=warmup)"
   ],
   "outputs": [],
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "27ASQD3q-M0A",
    "outputId": "04eec20c-51c3-4a71-e570-9636af9421c0",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:34:02.653259Z",
     "start_time": "2025-06-26T18:31:43.346852Z"
    }
   },
   "source": [
    "# Train NLP solution map\n",
    "best_model = trainer.train()\n",
    "best_outputs = trainer.test(best_model)\n",
    "# load best model dict\n",
    "problem.load_state_dict(best_model)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0  train_loss: 122.0165786743164\n",
      "epoch: 1  train_loss: 32.59976577758789\n",
      "epoch: 2  train_loss: 32.42778778076172\n",
      "epoch: 3  train_loss: 31.820749282836914\n",
      "epoch: 4  train_loss: 34.016841888427734\n",
      "epoch: 5  train_loss: 32.415164947509766\n",
      "epoch: 6  train_loss: 34.889774322509766\n",
      "epoch: 7  train_loss: 32.637351989746094\n",
      "epoch: 8  train_loss: 31.90712547302246\n",
      "epoch: 9  train_loss: 32.11321258544922\n",
      "epoch: 10  train_loss: 31.718921661376953\n",
      "epoch: 11  train_loss: 30.254234313964844\n",
      "epoch: 12  train_loss: 30.69602394104004\n",
      "epoch: 13  train_loss: 30.477611541748047\n",
      "epoch: 14  train_loss: 29.67075538635254\n",
      "epoch: 15  train_loss: 30.36856460571289\n",
      "epoch: 16  train_loss: 30.05189323425293\n",
      "epoch: 17  train_loss: 29.986268997192383\n",
      "epoch: 18  train_loss: 31.145029067993164\n",
      "epoch: 19  train_loss: 31.928056716918945\n",
      "epoch: 20  train_loss: 29.9550838470459\n",
      "epoch: 21  train_loss: 29.84864616394043\n",
      "epoch: 22  train_loss: 30.215057373046875\n",
      "epoch: 23  train_loss: 28.873130798339844\n",
      "epoch: 24  train_loss: 29.615304946899414\n",
      "epoch: 25  train_loss: 30.348560333251953\n",
      "epoch: 26  train_loss: 28.966590881347656\n",
      "epoch: 27  train_loss: 28.232952117919922\n",
      "epoch: 28  train_loss: 29.934934616088867\n",
      "epoch: 29  train_loss: 29.8786563873291\n",
      "epoch: 30  train_loss: 30.624427795410156\n",
      "epoch: 31  train_loss: 29.580942153930664\n",
      "epoch: 32  train_loss: 30.064910888671875\n",
      "epoch: 33  train_loss: 28.517440795898438\n",
      "epoch: 34  train_loss: 29.859216690063477\n",
      "epoch: 35  train_loss: 30.550840377807617\n",
      "epoch: 36  train_loss: 29.090612411499023\n",
      "epoch: 37  train_loss: 30.240283966064453\n",
      "epoch: 38  train_loss: 29.05544090270996\n",
      "epoch: 39  train_loss: 29.76102066040039\n",
      "epoch: 40  train_loss: 28.437641143798828\n",
      "epoch: 41  train_loss: 29.666259765625\n",
      "epoch: 42  train_loss: 29.59459114074707\n",
      "epoch: 43  train_loss: 29.402917861938477\n",
      "epoch: 44  train_loss: 30.635150909423828\n",
      "epoch: 45  train_loss: 28.985912322998047\n",
      "epoch: 46  train_loss: 28.251249313354492\n",
      "epoch: 47  train_loss: 27.743709564208984\n",
      "epoch: 48  train_loss: 29.46579360961914\n",
      "epoch: 49  train_loss: 28.81446075439453\n",
      "epoch: 50  train_loss: 29.479801177978516\n",
      "epoch: 51  train_loss: 28.35890007019043\n",
      "epoch: 52  train_loss: 30.075580596923828\n",
      "epoch: 53  train_loss: 29.701101303100586\n",
      "epoch: 54  train_loss: 29.359128952026367\n",
      "epoch: 55  train_loss: 28.803970336914062\n",
      "epoch: 56  train_loss: 29.049415588378906\n",
      "epoch: 57  train_loss: 28.837543487548828\n",
      "epoch: 58  train_loss: 29.581167221069336\n",
      "epoch: 59  train_loss: 28.93110466003418\n",
      "epoch: 60  train_loss: 28.82439613342285\n",
      "epoch: 61  train_loss: 28.539623260498047\n",
      "epoch: 62  train_loss: 28.467763900756836\n",
      "epoch: 63  train_loss: 29.61488151550293\n",
      "epoch: 64  train_loss: 29.401235580444336\n",
      "epoch: 65  train_loss: 29.44623374938965\n",
      "epoch: 66  train_loss: 28.89919090270996\n",
      "epoch: 67  train_loss: 29.776588439941406\n",
      "epoch: 68  train_loss: 29.340303421020508\n",
      "epoch: 69  train_loss: 29.151416778564453\n",
      "epoch: 70  train_loss: 28.91688346862793\n",
      "epoch: 71  train_loss: 28.1113224029541\n",
      "epoch: 72  train_loss: 29.162261962890625\n",
      "epoch: 73  train_loss: 28.953065872192383\n",
      "epoch: 74  train_loss: 28.862892150878906\n",
      "epoch: 75  train_loss: 29.148805618286133\n",
      "epoch: 76  train_loss: 28.79718589782715\n",
      "epoch: 77  train_loss: 29.873960494995117\n",
      "epoch: 78  train_loss: 28.78235626220703\n",
      "epoch: 79  train_loss: 28.737060546875\n",
      "epoch: 80  train_loss: 28.739503860473633\n",
      "epoch: 81  train_loss: 29.89686393737793\n",
      "epoch: 82  train_loss: 28.856796264648438\n",
      "epoch: 83  train_loss: 28.911914825439453\n",
      "epoch: 84  train_loss: 28.557723999023438\n",
      "epoch: 85  train_loss: 29.353302001953125\n",
      "epoch: 86  train_loss: 29.10129165649414\n",
      "epoch: 87  train_loss: 29.044740676879883\n",
      "epoch: 88  train_loss: 28.8603515625\n",
      "epoch: 89  train_loss: 30.085542678833008\n",
      "epoch: 90  train_loss: 28.444177627563477\n",
      "epoch: 91  train_loss: 29.74268341064453\n",
      "epoch: 92  train_loss: 28.3732967376709\n",
      "epoch: 93  train_loss: 28.897279739379883\n",
      "epoch: 94  train_loss: 29.15414047241211\n",
      "epoch: 95  train_loss: 29.812536239624023\n",
      "epoch: 96  train_loss: 27.732961654663086\n",
      "epoch: 97  train_loss: 28.785995483398438\n",
      "epoch: 98  train_loss: 29.673583984375\n",
      "epoch: 99  train_loss: 29.092472076416016\n",
      "epoch: 100  train_loss: 28.285062789916992\n",
      "epoch: 101  train_loss: 28.441608428955078\n",
      "epoch: 102  train_loss: 28.256132125854492\n",
      "epoch: 103  train_loss: 28.224063873291016\n",
      "epoch: 104  train_loss: 29.050783157348633\n",
      "epoch: 105  train_loss: 29.6864070892334\n",
      "epoch: 106  train_loss: 28.173851013183594\n",
      "epoch: 107  train_loss: 28.19454574584961\n",
      "epoch: 108  train_loss: 28.51160430908203\n",
      "epoch: 109  train_loss: 28.5582275390625\n",
      "epoch: 110  train_loss: 30.819477081298828\n",
      "epoch: 111  train_loss: 27.86980628967285\n",
      "epoch: 112  train_loss: 29.07628059387207\n",
      "epoch: 113  train_loss: 28.436819076538086\n",
      "epoch: 114  train_loss: 28.10340118408203\n",
      "epoch: 115  train_loss: 28.682754516601562\n",
      "epoch: 116  train_loss: 27.929903030395508\n",
      "epoch: 117  train_loss: 27.58698081970215\n",
      "epoch: 118  train_loss: 29.16461753845215\n",
      "epoch: 119  train_loss: 28.433319091796875\n",
      "epoch: 120  train_loss: 29.216960906982422\n",
      "epoch: 121  train_loss: 28.485824584960938\n",
      "epoch: 122  train_loss: 29.013418197631836\n",
      "epoch: 123  train_loss: 28.290420532226562\n",
      "epoch: 124  train_loss: 29.23430061340332\n",
      "epoch: 125  train_loss: 28.42031478881836\n",
      "epoch: 126  train_loss: 27.42031478881836\n",
      "epoch: 127  train_loss: 28.228130340576172\n",
      "epoch: 128  train_loss: 28.388141632080078\n",
      "epoch: 129  train_loss: 29.2607479095459\n",
      "epoch: 130  train_loss: 27.991851806640625\n",
      "epoch: 131  train_loss: 29.37331771850586\n",
      "epoch: 132  train_loss: 29.048057556152344\n",
      "epoch: 133  train_loss: 28.794597625732422\n",
      "epoch: 134  train_loss: 28.079208374023438\n",
      "epoch: 135  train_loss: 28.23906898498535\n",
      "epoch: 136  train_loss: 29.0970516204834\n",
      "epoch: 137  train_loss: 28.411680221557617\n",
      "epoch: 138  train_loss: 28.896486282348633\n",
      "epoch: 139  train_loss: 28.384729385375977\n",
      "epoch: 140  train_loss: 27.838781356811523\n",
      "epoch: 141  train_loss: 28.88416862487793\n",
      "epoch: 142  train_loss: 28.422019958496094\n",
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      "epoch: 146  train_loss: 27.944623947143555\n",
      "epoch: 147  train_loss: 28.23923110961914\n",
      "epoch: 148  train_loss: 28.61170196533203\n",
      "epoch: 149  train_loss: 28.848472595214844\n",
      "epoch: 150  train_loss: 27.804668426513672\n",
      "epoch: 151  train_loss: 28.352445602416992\n",
      "epoch: 152  train_loss: 28.24355697631836\n",
      "epoch: 153  train_loss: 27.97037124633789\n",
      "epoch: 154  train_loss: 29.94175910949707\n",
      "epoch: 155  train_loss: 28.203054428100586\n",
      "epoch: 156  train_loss: 28.203590393066406\n",
      "epoch: 157  train_loss: 28.96696662902832\n",
      "epoch: 158  train_loss: 28.5402774810791\n",
      "epoch: 159  train_loss: 27.971113204956055\n",
      "epoch: 160  train_loss: 29.227968215942383\n",
      "epoch: 161  train_loss: 28.691513061523438\n",
      "epoch: 162  train_loss: 27.71091651916504\n",
      "epoch: 163  train_loss: 28.007455825805664\n",
      "epoch: 164  train_loss: 28.72826385498047\n",
      "epoch: 165  train_loss: 28.666095733642578\n",
      "epoch: 166  train_loss: 28.041223526000977\n",
      "epoch: 167  train_loss: 27.98076057434082\n",
      "epoch: 168  train_loss: 29.528749465942383\n",
      "epoch: 169  train_loss: 27.77448844909668\n",
      "epoch: 170  train_loss: 29.060171127319336\n",
      "epoch: 171  train_loss: 27.816268920898438\n",
      "epoch: 172  train_loss: 29.029380798339844\n",
      "epoch: 173  train_loss: 28.49034881591797\n",
      "epoch: 174  train_loss: 28.628690719604492\n",
      "epoch: 175  train_loss: 28.587970733642578\n",
      "epoch: 176  train_loss: 28.372480392456055\n",
      "epoch: 177  train_loss: 29.365032196044922\n",
      "epoch: 178  train_loss: 28.148313522338867\n",
      "epoch: 179  train_loss: 27.953998565673828\n",
      "epoch: 180  train_loss: 28.091659545898438\n",
      "epoch: 181  train_loss: 28.188440322875977\n",
      "epoch: 182  train_loss: 28.207155227661133\n",
      "epoch: 183  train_loss: 27.738052368164062\n",
      "epoch: 184  train_loss: 28.54937171936035\n",
      "epoch: 185  train_loss: 28.05453872680664\n",
      "epoch: 186  train_loss: 28.492067337036133\n",
      "epoch: 187  train_loss: 27.730567932128906\n",
      "epoch: 188  train_loss: 27.969913482666016\n",
      "epoch: 189  train_loss: 28.021493911743164\n",
      "epoch: 190  train_loss: 28.42604637145996\n",
      "epoch: 191  train_loss: 28.0308837890625\n",
      "epoch: 192  train_loss: 27.4707088470459\n",
      "epoch: 193  train_loss: 28.242122650146484\n",
      "epoch: 194  train_loss: 28.105243682861328\n",
      "epoch: 195  train_loss: 28.137170791625977\n",
      "epoch: 196  train_loss: 29.270055770874023\n",
      "epoch: 197  train_loss: 28.438756942749023\n",
      "epoch: 198  train_loss: 28.854463577270508\n",
      "epoch: 199  train_loss: 28.41814613342285\n",
      "epoch: 200  train_loss: 28.240150451660156\n",
      "epoch: 201  train_loss: 29.55333137512207\n",
      "Early stopping!!!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g-KmqqUj-Q3E"
   },
   "source": [
    "## Get a QP solution from CVXPY as a benchmark\n",
    "\n",
    "[CVXPY](https://www.cvxpy.org/) is an open-source tool for constrained optimization that has influenced the development of NeuroMANCER."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "dmJERFP2yYuC",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:34:02.732344Z",
     "start_time": "2025-06-26T18:34:02.717295Z"
    }
   },
   "source": [
    "# Define the CVXPY problems.\n",
    "def QP_param(p1, p2):\n",
    "    x = cp.Variable(1)\n",
    "    y = cp.Variable(1)\n",
    "    prob = cp.Problem(cp.Minimize(x ** 2 + y ** 2),\n",
    "                      [-x - y + p1 <= 0,\n",
    "                       x + y - p1 - 5 <= 0,\n",
    "                       x - y + p2 - 5 <= 0,\n",
    "                       -x + y - p2 <= 0])\n",
    "    return prob, x, y\n",
    "\n",
    "def QCQP_param(p1, p2):\n",
    "    x = cp.Variable(1)\n",
    "    y = cp.Variable(1)\n",
    "    prob = cp.Problem(cp.Minimize(x ** 2 + y ** 2),\n",
    "                  [-x - y + p1 <= 0,\n",
    "                   x ** 2 + y ** 2 - p2 ** 2 <= 0])\n",
    "    return prob, x, y"
   ],
   "outputs": [],
   "execution_count": 13
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pcNq5fOVE4lR"
   },
   "source": [
    "## Compare: NeuroMANCER vs. CVXPY"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "id": "kvYCfjq6zxxC",
    "outputId": "322e5b72-b93d-4d9e-a913-8703aad67d1a",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:34:03.839410Z",
     "start_time": "2025-06-26T18:34:02.732344Z"
    }
   },
   "source": [
    "\"\"\"\n",
    "Plots\n",
    "\"\"\"\n",
    "# test problem parameters\n",
    "params = [2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]\n",
    "x1 = np.arange(-1.0, 10.0, 0.05)\n",
    "y1 = np.arange(-1.0, 10.0, 0.05)\n",
    "xx, yy = np.meshgrid(x1, y1)\n",
    "fig, ax = plt.subplots(3,3)\n",
    "row_id = 0\n",
    "column_id = 0\n",
    "for i, p in enumerate(params):\n",
    "    if i % 3 == 0 and i != 0:\n",
    "        row_id += 1\n",
    "        column_id = 0\n",
    "\n",
    "    # eval and plot objective and constraints\n",
    "    J = xx ** 2 + yy ** 2\n",
    "    cp_plot = ax[row_id, column_id].contourf(xx, yy, J, 50, alpha=0.4)\n",
    "    ax[row_id, column_id].set_title(f'QP p={p}')\n",
    "    if problem_type == 'pQP':  # constraints for QP\n",
    "        c1 = xx + yy - p\n",
    "        c2 = -xx - yy + p + 5\n",
    "        c3 = -xx + yy - p + 5\n",
    "        c4 = xx - yy + p\n",
    "        cg1 = ax[row_id, column_id].contour(xx, yy, c1, [0], colors='mediumblue', alpha=0.7)\n",
    "        cg2 = ax[row_id, column_id].contour(xx, yy, c2, [0], colors='mediumblue', alpha=0.7)\n",
    "        cg3 = ax[row_id, column_id].contour(xx, yy, c3, [0], colors='mediumblue', alpha=0.7)\n",
    "        cg4 = ax[row_id, column_id].contour(xx, yy, c4, [0], colors='mediumblue', alpha=0.7)\n",
    "\n",
    "        if hasattr(cg1, 'collections') and cg1.collections:\n",
    "            plt.setp(cg1.collections,\n",
    "                     path_effects=[patheffects.withTickedStroke()], alpha=0.7)\n",
    "            plt.setp(cg2.collections,\n",
    "                     path_effects=[patheffects.withTickedStroke()], alpha=0.7)\n",
    "            plt.setp(cg3.collections,\n",
    "                     path_effects=[patheffects.withTickedStroke()], alpha=0.7)\n",
    "            plt.setp(cg4.collections,\n",
    "                     path_effects=[patheffects.withTickedStroke()], alpha=0.7)\n",
    "\n",
    "    if problem_type == 'pQCQP':  # constraints for QCQP\n",
    "        c1 = xx + yy - p\n",
    "        c2 = - xx**2 - yy**2 + p**2\n",
    "        cg1 = ax[row_id, column_id].contour(xx, yy, c1, [0], colors='mediumblue', alpha=0.7)\n",
    "        cg2 = ax[row_id, column_id].contour(xx, yy, c2, [0], colors='mediumblue', alpha=0.7)\n",
    "        if hasattr(cg1, 'collections') and cg1.collections:\n",
    "            plt.setp(cg1.collections,\n",
    "                     path_effects=[patheffects.withTickedStroke()], alpha=0.7)\n",
    "            plt.setp(cg2.collections,\n",
    "                     path_effects=[patheffects.withTickedStroke()], alpha=0.7)\n",
    "    fig.colorbar(cp_plot, ax=ax[row_id,column_id])\n",
    "\n",
    "    # Solve CVXPY problem\n",
    "    if problem_type == 'pQP':\n",
    "        prob, x, y = QP_param(p, p)\n",
    "    elif problem_type == 'pQCQP':\n",
    "        prob, x, y = QCQP_param(p, p)\n",
    "    prob.solve()\n",
    "\n",
    "    # Solve via neuromancer\n",
    "    datapoint = {'p1': torch.tensor([[p]]), 'p2': torch.tensor([[p]]),\n",
    "                 'name': 'test'}\n",
    "    model_out = problem(datapoint)\n",
    "    x_nm = model_out['test_' + \"x\"][0, 0].detach().numpy()\n",
    "    y_nm = model_out['test_' + \"x\"][0, 1].detach().numpy()\n",
    "\n",
    "    print(f'primal solution {problem_type} x={x.value}, y={y.value}')\n",
    "    print(f'parameter p={p, p}')\n",
    "    print(f'primal solution Neuromancer x1={x_nm}, x2={y_nm}')\n",
    "    print(f' f: {model_out[\"test_\" + f.key]}')\n",
    "    print(f' g1: {model_out[\"test_\" + g1.key]}')\n",
    "    print(f' g2: {model_out[\"test_\" + g2.key]}')\n",
    "    if problem_type == 'pQP':\n",
    "        print(f' g3: {model_out[\"test_\" + g3.key]}')\n",
    "        print(f' g4: {model_out[\"test_\" + g4.key]}')\n",
    "\n",
    "    # Plot optimal solutions\n",
    "    ax[row_id, column_id].plot(x.value, y.value, 'g*', markersize=10)\n",
    "    ax[row_id, column_id].plot(x_nm, y_nm, 'r*', markersize=10)\n",
    "    column_id += 1\n",
    "plt.show()\n",
    "plt.show(block=True)\n",
    "plt.interactive(False)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "primal solution pQP x=[1.], y=[1.]\n",
      "parameter p=(2.0, 2.0)\n",
      "primal solution Neuromancer x1=1.2035504579544067, x2=0.8227365612983704\n",
      " f: tensor([[2.1254]], grad_fn=<AddBackward0>)\n",
      " g1: tensor([[-0.0263]], grad_fn=<AddBackward0>)\n",
      " g2: tensor([[-4.9737]], grad_fn=<SubBackward0>)\n",
      " g3: tensor([[-2.6192]], grad_fn=<SubBackward0>)\n",
      " g4: tensor([[-2.3808]], grad_fn=<SubBackward0>)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\drgon\\AppData\\Local\\Temp\\ipykernel_59452\\2728073885.py:30: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed in 3.10.\n",
      "  plt.setp(cg1.collections,\n",
      "C:\\Users\\drgon\\AppData\\Local\\Temp\\ipykernel_59452\\2728073885.py:32: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed in 3.10.\n",
      "  plt.setp(cg2.collections,\n",
      "C:\\Users\\drgon\\AppData\\Local\\Temp\\ipykernel_59452\\2728073885.py:34: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed in 3.10.\n",
      "  plt.setp(cg3.collections,\n",
      "C:\\Users\\drgon\\AppData\\Local\\Temp\\ipykernel_59452\\2728073885.py:36: MatplotlibDeprecationWarning: The collections attribute was deprecated in Matplotlib 3.8 and will be removed in 3.10.\n",
      "  plt.setp(cg4.collections,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "primal solution pQP x=[1.5], y=[1.5]\n",
      "parameter p=(3.0, 3.0)\n",
      "primal solution Neuromancer x1=1.6041899919509888, x2=1.3785439729690552\n",
      " f: tensor([[4.4738]], grad_fn=<AddBackward0>)\n",
      " g1: tensor([[0.0173]], grad_fn=<AddBackward0>)\n",
      " g2: tensor([[-5.0173]], grad_fn=<SubBackward0>)\n",
      " g3: tensor([[-1.7744]], grad_fn=<SubBackward0>)\n",
      " g4: tensor([[-3.2256]], grad_fn=<SubBackward0>)\n",
      "primal solution pQP x=[2.], y=[2.]\n",
      "parameter p=(4.0, 4.0)\n",
      "primal solution Neuromancer x1=1.9988573789596558, x2=2.0022976398468018\n",
      " f: tensor([[8.0046]], grad_fn=<AddBackward0>)\n",
      " g1: tensor([[-0.0012]], grad_fn=<AddBackward0>)\n",
      " g2: tensor([[-4.9988]], grad_fn=<SubBackward0>)\n",
      " g3: tensor([[-1.0034]], grad_fn=<SubBackward0>)\n",
      " g4: tensor([[-3.9966]], grad_fn=<SubBackward0>)\n",
      "primal solution pQP x=[2.5], y=[2.5]\n",
      "parameter p=(5.0, 5.0)\n",
      "primal solution Neuromancer x1=2.2224645614624023, x2=2.778649091720581\n",
      " f: tensor([[12.6602]], grad_fn=<AddBackward0>)\n",
      " g1: tensor([[-0.0011]], grad_fn=<AddBackward0>)\n",
      " g2: tensor([[-4.9989]], grad_fn=<SubBackward0>)\n",
      " g3: tensor([[-0.5562]], grad_fn=<SubBackward0>)\n",
      " g4: tensor([[-4.4438]], grad_fn=<SubBackward0>)\n",
      "primal solution pQP x=[2.5], y=[3.5]\n",
      "parameter p=(6.0, 6.0)\n",
      "primal solution Neuromancer x1=2.2957606315612793, x2=3.7111783027648926\n",
      " f: tensor([[19.0434]], grad_fn=<AddBackward0>)\n",
      " g1: tensor([[-0.0069]], grad_fn=<AddBackward0>)\n",
      " g2: tensor([[-4.9931]], grad_fn=<SubBackward0>)\n",
      " g3: tensor([[-0.4154]], grad_fn=<SubBackward0>)\n",
      " g4: tensor([[-4.5846]], grad_fn=<SubBackward0>)\n",
      "primal solution pQP x=[2.5], y=[4.5]\n",
      "parameter p=(7.0, 7.0)\n",
      "primal solution Neuromancer x1=2.3498849868774414, x2=4.663606643676758\n",
      " f: tensor([[27.2712]], grad_fn=<AddBackward0>)\n",
      " g1: tensor([[-0.0135]], grad_fn=<AddBackward0>)\n",
      " g2: tensor([[-4.9865]], grad_fn=<SubBackward0>)\n",
      " g3: tensor([[-0.3137]], grad_fn=<SubBackward0>)\n",
      " g4: tensor([[-4.6863]], grad_fn=<SubBackward0>)\n",
      "primal solution pQP x=[2.5], y=[5.5]\n",
      "parameter p=(8.0, 8.0)\n",
      "primal solution Neuromancer x1=2.383965253829956, x2=5.63942813873291\n",
      " f: tensor([[37.4864]], grad_fn=<AddBackward0>)\n",
      " g1: tensor([[-0.0234]], grad_fn=<AddBackward0>)\n",
      " g2: tensor([[-4.9766]], grad_fn=<SubBackward0>)\n",
      " g3: tensor([[-0.2555]], grad_fn=<SubBackward0>)\n",
      " g4: tensor([[-4.7445]], grad_fn=<SubBackward0>)\n",
      "primal solution pQP x=[2.5], y=[6.5]\n",
      "parameter p=(9.0, 9.0)\n",
      "primal solution Neuromancer x1=2.4169845581054688, x2=6.616637706756592\n",
      " f: tensor([[49.6217]], grad_fn=<AddBackward0>)\n",
      " g1: tensor([[-0.0336]], grad_fn=<AddBackward0>)\n",
      " g2: tensor([[-4.9664]], grad_fn=<SubBackward0>)\n",
      " g3: tensor([[-0.1997]], grad_fn=<SubBackward0>)\n",
      " g4: tensor([[-4.8003]], grad_fn=<SubBackward0>)\n",
      "primal solution pQP x=[2.5], y=[7.5]\n",
      "parameter p=(10.0, 10.0)\n",
      "primal solution Neuromancer x1=2.450514316558838, x2=7.592984199523926\n",
      " f: tensor([[63.6584]], grad_fn=<AddBackward0>)\n",
      " g1: tensor([[-0.0435]], grad_fn=<AddBackward0>)\n",
      " g2: tensor([[-4.9565]], grad_fn=<SubBackward0>)\n",
      " g3: tensor([[-0.1425]], grad_fn=<SubBackward0>)\n",
      " g4: tensor([[-4.8575]], grad_fn=<SubBackward0>)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 18 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "yD5kAnjy4CUL",
    "ExecuteTime": {
     "end_time": "2025-06-26T18:45:58.192508Z",
     "start_time": "2025-06-26T18:45:07.852769Z"
    }
   },
   "source": [
    "\"\"\"\n",
    "Benchmark Solution\n",
    "\"\"\"\n",
    "\n",
    "def eval_constraints(x, y, p1, p2):\n",
    "    \"\"\"\n",
    "    evaluate mean constraints violations\n",
    "    \"\"\"\n",
    "    con_1_viol = np.maximum(0, -x - y + p1)\n",
    "    con_2_viol = np.maximum(0, x + y - p1 - 5)\n",
    "    con_3_viol = np.maximum(0, x - y + p2 - 5)\n",
    "    con_4_viol = np.maximum(0, -x + y - p2)\n",
    "    con_viol = con_1_viol + con_2_viol + con_3_viol + con_4_viol\n",
    "    con_viol_mean = np.mean(con_viol)\n",
    "    return con_viol_mean\n",
    "\n",
    "def eval_objective(x, y, a1=1, a2=1):\n",
    "    obj_value_mean = np.mean(a1 * x**2 + a2 * y**2)\n",
    "    return obj_value_mean\n",
    "\n",
    "# Solve via neuromancer\n",
    "t = time.time()\n",
    "samples_test['name'] = 'test'\n",
    "model_out = problem(samples_test)\n",
    "nm_time = time.time() - t\n",
    "x_nm = model_out['test_' + \"x\"][:, [0]].detach().numpy()\n",
    "y_nm = model_out['test_' + \"x\"][:, [1]].detach().numpy()\n",
    "\n",
    "# Solve via solver\n",
    "t = time.time()\n",
    "x_solver, y_solver = [], []\n",
    "P1, P2 = [], []\n",
    "for i in range(0, nsim):\n",
    "    p1 = samples_test['p1'][i].detach().numpy()\n",
    "    p2 = samples_test['p2'][i].detach().numpy()\n",
    "    prob, x, y = QP_param(p1, p2)\n",
    "    prob.solve(solver='OSQP', verbose=False)\n",
    "    prob.solve()\n",
    "    x_solver.append(x.value)\n",
    "    y_solver.append(y.value)\n",
    "    P1.append(p1)\n",
    "    P2.append(p2)\n",
    "solver_time = time.time() - t\n",
    "x_solver = np.asarray(x_solver)\n",
    "y_solver = np.asarray(y_solver)\n",
    "P1 = np.asarray(P1)\n",
    "P2 = np.asarray(P2)\n",
    "\n",
    "# Evaluate neuromancer solution\n",
    "print(f'Solution for {nsim} problems via Neuromancer obtained in {nm_time:.4f} seconds')\n",
    "nm_con_viol_mean = eval_constraints(x_nm, y_nm, P1, P2)\n",
    "print(f'Neuromancer mean constraints violation {nm_con_viol_mean:.4f}')\n",
    "nm_obj_mean = eval_objective(x_nm, y_nm)\n",
    "print(f'Neuromancer mean objective value {nm_obj_mean:.4f}')\n",
    "\n",
    "# Evaluate solver solution\n",
    "print(f'Solution for {nsim} problems via solver obtained in {solver_time:.4f} seconds')\n",
    "solver_con_viol_mean = eval_constraints(x_solver, y_solver, P1, P2)\n",
    "print(f'Solver mean constraints violation {solver_con_viol_mean:.4f}')\n",
    "solver_obj_mean = eval_objective(x_solver, y_solver)\n",
    "print(f'Solver mean objective value {solver_obj_mean:.4f}')\n",
    "\n",
    "# neuromancer solver comparison\n",
    "speedup_factor = solver_time/nm_time\n",
    "print(f'Solution speedup factor {speedup_factor:.4f}')\n",
    "\n",
    "# Difference in primal optimizers\n",
    "dx = (x_solver - x_nm)[:,0]\n",
    "dy = (y_solver - y_nm)[:,0]\n",
    "err_x = np.mean(dx**2)\n",
    "err_y = np.mean(dy**2)\n",
    "err_primal = err_x + err_y\n",
    "print('MSE primal optimizers:', err_primal)\n",
    "\n",
    "# Difference in objective\n",
    "err_obj = np.abs(solver_obj_mean - nm_obj_mean) / solver_obj_mean * 100\n",
    "print(f'mean objective value discrepancy: {err_obj:.2f} %')\n",
    "\n",
    "# stats to log\n",
    "stats = {\"nsim\": nsim,\n",
    "         \"nm_time\": nm_time,\n",
    "         \"nm_con_viol_mean\": nm_con_viol_mean,\n",
    "         \"nm_obj_mean\": nm_obj_mean,\n",
    "         \"solver_time\": solver_time,\n",
    "         \"solver_con_viol_mean\": solver_con_viol_mean,\n",
    "         \"solver_obj_mean\": solver_obj_mean,\n",
    "         \"speedup_factor\": speedup_factor,\n",
    "         \"err_primal\": err_primal,\n",
    "         \"err_obj\": err_obj}"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solution for 3000 problems via Neuromancer obtained in 0.0157 seconds\n",
      "Neuromancer mean constraints violation 0.0210\n",
      "Neuromancer mean objective value 26.5845\n",
      "Solution for 3000 problems via solver obtained in 50.2921 seconds\n",
      "Solver mean constraints violation 0.0000\n",
      "Solver mean objective value 26.2859\n",
      "Solution speedup factor 3197.8071\n",
      "MSE primal optimizers: 0.031133980584664066\n",
      "mean objective value discrepancy: 1.14 %\n"
     ]
    }
   ],
   "execution_count": 16
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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